for i in range(pmask.sum()):
objects.append({
'm':
float(data[pmask]['pl_bmassj'].iloc[i]) * mjup / msun,
'P':
float(data[pmask]['pl_orbper'].iloc[i]),
'inc':
3.14159 / 2,
'e':
float(data[pmask]['pl_orbeccen'].iloc[i]),
'omega':
0
})
# create REBOUND simulation
sim = generate(objects)
print(objects)
# year long integrations, timestep = 1 hour
sim_data = integrate(sim, objects, 20 * objects[-1]['P'],
int(20 * objects[-1]['P'] * 24))
# collect the analytics from the simulation
ttv_data = analyze(sim_data)
#if name=='Kepler-30':
# import pdb; pdb.set_trace()
# save simulation data
for i in range(pmask.sum()):
iname = data[pmask].iloc[i].name
def nbody_limits(newobj, nlstats, n=1):
# TODO create wrapper?
upper = np.copy(ttv)
lower = np.copy(ttv)
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] - n * nlstats['marginals'][3]['sigma']
obj[2]['omega'] = nlstats['marginals'][5][
'median'] - n * nlstats['marginals'][5]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] - n * nlstats['marginals'][3]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] + n * nlstats['marginals'][3]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] - n * nlstats['marginals'][3]['sigma']
obj[2]['omega'] = nlstats['marginals'][5][
'median'] + n * nlstats['marginals'][5]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] + n * nlstats['marginals'][3]['sigma']
obj[2]['omega'] = nlstats['marginals'][5][
'median'] - n * nlstats['marginals'][5]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
obj = copy.deepcopy(newobj)
obj[2]['m'] = nlstats['marginals'][3][
'median'] + n * nlstats['marginals'][3]['sigma']
obj[2]['omega'] = nlstats['marginals'][5][
'median'] + n * nlstats['marginals'][5]['sigma']
sim_data = generate(obj, newobj[1]['P'] * (len(ttv) + 1),
int((len(ttv) + 1) * newobj[1]['P'] * 24))
ttv_data = analyze(sim_data)
for i in range(len(ttv_data['planets'][0]['ttv'])):
try:
upper[i] = max(upper[i], ttv_data['planets'][0]['ttv'][i])
lower[i] = min(lower[i], ttv_data['planets'][0]['ttv'][i])
except:
pass
return upper, lower
def get_ttv(objects, ndays=60, ttvfast=True):
sim = generate(objects)
sim_data = integrate(sim, objects, ndays, ndays * 24 * 2) # dt=30 minutes
return analyze(sim_data, ttvfast=ttvfast)
# plot the results
#report(ttv_data)
periods = np.linspace(1.25*objects[1]['P'], 2.2*objects[1]['P'], 50 )
#masses = np.linspace( objects[1]['m']*0.25, objects[1]['m']*6, 100 )
masses = np.linspace( mearth/msun, 20*mearth/msun, 50 )
ttvs = np.zeros((periods.shape[0],masses.shape[0]))
for i in range(periods.shape[0]):
print(i)
for j in range(masses.shape[0]):
objects[2]['P'] = periods[i]
objects[2]['m'] = masses[j]
sim_data = generate(objects, 180, 180*24)
ttv_data = analyze(sim_data)
ttvs[i,j] = ttv_data['planets'][0]['max']*24*60
top = cm.get_cmap('viridis', 200)
newcolors = top(np.linspace(0., 1, 200))
newcolors[int(200* 2./30):int(200* 5./30), 3] = np.linspace(0,1,int(200* 5./30)-int(200* 2./30) ) # faded 1-3
newcolors[:int(200* 2./30), 3] = 0 # white under 1 minutes
newcmp = ListedColormap(newcolors)
plt.imshow(ttvs, vmin=0, vmax=30, cmap=newcmp, origin='lower',
extent=[ min(masses)*msun/mearth, max(masses)*msun/mearth, min(periods)/objects[1]['P'],max(periods)/objects[1]['P']],
#'omega':[ objects[2]['omega']-1, objects[2]['omega']+1 ],
}),
]
print(bounds)
newobj, nlposteriors, nlstats = nlfit(
data[~bmask,0], data[~bmask,1], data[~bmask,2],
objects, bounds,
myloss='linear',
)
# O-C Model #######################################################################################
ocdata_l = data[:,1] - ( data[:,0]*lstats['marginals'][1]['median'] + lstats['marginals'][0]['median'] )
ocdata_nl = data[:,1] - ( data[:,0]*nlstats['marginals'][1]['median'] + nlstats['marginals'][0]['median'] )
sim_data = generate(newobj, newobj[1]['P']* (data[:,0].max()+1), int( (data[:,0].max()+1)*newobj[1]['P']*24) )
ttv_data = analyze(sim_data)
upper, lower = nbody_limits( newobj, nlstats, ttv_data['planets'][0]['ttv'] )
f,ax = plt.subplots(1, figsize=(7,4))
ax.errorbar(data[:,0],ocdata_nl*24*60,yerr=data[:,2]*24*60,ls='none',marker='.',label='Data',color='black')
ax.plot( ttv_data['planets'][0]['ttv']*24*60, label='Linear+Nbody ({:.1f})'.format(nlstats['global evidence']),color='red')
ax.fill_between( np.arange(upper.shape[0]),24*60*upper,24*60*lower,alpha=0.1,label='Nbody 3 sigma',color='red')
ax.axhline(ls='--',label='Linear ({:.1f})'.format(lstats['global evidence']))
ax.legend(loc='best')
ax.set_xlabel('Epochs')
ax.set_xlim([min(data[:,0]),max(data[:,0])])
ax.set_ylim([min(ocdata_nl)*24*60-1,max(ocdata_nl)*24*60+1])
ax.set_ylabel('O-C [min]')
ax.grid(True)
plt.savefig('ttv_model.pdf',bbox_inches='tight')
parser.add_argument("-o", "--omegas", help=help_, default=10, type=int)
help_ = "Number of planet 1 orbital periods to integrate the simulation for"
parser.add_argument("-p", "--periods", help=help_, default=30, type=int)
args = parser.parse_args()
try:
X, y = pickle.load(open(args.file, 'rb'))
except:
X, y = [], []
# seed simulation
og_objects = randomize()
sim_data = generate(
og_objects,
Ndays=og_objects[1]['P'] * args.periods + 1,
Noutputs=round(og_objects[1]['P'] * args.periods * 24 +
24) # dt = 1 hr
)
ttv_data = analyze(sim_data)
# report(ttv_data)
for ii in range(len(X), args.samples):
print('simulation:', ii)
# randomize objec ts
og_objects = randomize()
sim_data = generate(
og_objects,
Ndays=og_objects[1]['P'] * args.periods + 1,
Noutputs=round(og_objects[1]['P'] * args.periods * 24 +
{
'm': args.mass1,
'P': args.period1,
'inc': 3.14159 / 2,
'e': 0,
'omega': 0
},
{
'm': (ypred[i, 1]) * mearth / msun,
'P': ypred[i, 0],
'inc': 3.14159 / 2,
'e': ypred[i, 3],
'omega': ypred[i, 2]
},
]
sim_data = generate(pred, max(data[:, 1]), int(max(data[:, 1]) * 24))
ttv_pred = analyze(sim_data)
ml_error = lambda x: x # TODO
nlstats = {
'marginals': [
{
'median': 0,
'sigma': 0
}, # Tmid
{
'median': pred[1]['P'],
'sigma': 0
}, # P1
{
'inc': 3.14159 / 2,
'e': 0,
'omega': 0
},
{
'm': 40 * mearth / msun,
'P': 7,
'inc': 3.14159 / 2,
'e': 0,
'omega': 0
},
#{'m':0.432*mjup/msun, 'P':12, 'inc':3.14159/2, 'e':0, 'omega':0 },
]
# create REBOUND simulation
sim_data = generate(objects, objects[1]['P'] * 30,
int(30 * objects[1]['P'] * 24))
# collect the analytics of interest from the simulation
ttv_data = analyze(sim_data)
# plot the results
report(ttv_data, savefile='report.png')
import numpy as np
ttv = ttv_data['planets'][0]['ttv']
epochs = np.arange(len(ttv))
ttdata = ttv_data['planets'][0]['tt'] + np.random.normal(
0, 0.5, len(ttv)) / (24 * 60)
err = (30 + np.random.normal(30, 30, len(ttv))) / (24 * 60 * 60)
np.vstack([epochs, ttdata, err]).T
np.savetxt('sim_data.txt',
'inc': [np.pi / 2 * 0.8, np.pi / 2 * 0.9],
'e': [0.025, 0.05],
'omega': [np.pi / 4, np.pi / 2],
# add longitude of ascending node
},
{
'm': [2 * mjup / msun, 10 * mjup / msun],
'P': [6, 9],
'inc': [np.pi / 2 * 0.8, np.pi / 2 * 0.9],
'e': [0.05, 0.075],
'omega': [np.pi / 4, np.pi / 2],
},
]
# run original simulation
sim = generate(objects)
sim_data = integrate(sim, objects, 180, 180 *
24) # year long integrations, timestep = 1 minute
ttv_data_og = analyze(sim_data)
# create a plot
f = plt.figure(figsize=(9, 13))
plt.subplots_adjust()
ax = [
plt.subplot2grid((6, 2), (0, 0)),
plt.subplot2grid((6, 2), (1, 0)),
plt.subplot2grid((6, 2), (2, 0)),
plt.subplot2grid((6, 2), (3, 0)),
plt.subplot2grid((6, 2), (4, 0)),
plt.subplot2grid((6, 2), (5, 0)),
plt.subplot2grid((6, 2), (1, 1)),
'm': 1.12
}, {
'm': 0.25 * mjup / msun,
'inc': 1.570795,
'e': 0,
'P': 3.2887967652699728
}, {
'e': 0.06,
'inc': 1.570795,
'm': 100 * mearth / msun,
'P': 7.29,
'omega': np.pi / 3,
}]
# create REBOUND simulation
sim_data = generate(objects, 31 * objects[1]['P'],
int(31 * objects[1]['P'] * 24))
ttv_data = analyze(sim_data)
report(ttv_data)
# simulate some observational data with noise
ttv = ttv_data['planets'][0]['ttv']
epochs = np.arange(len(ttv))
ttdata = ttv_data['planets'][0]['tt'] + np.random.normal(
0, 0.5, len(ttv)) / (24 * 60)
err = np.random.normal(90, 30, len(ttv)) / (24 * 60 * 60)
# perform nested sampling linear fit to transit data
lstats, lposteriors = lfit(epochs,
ttdata,
err,
bounds=[